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Soumya Sharma

Doctorat - McGill
Superviseur⋅e principal⋅e
Golnoosh Farnadi
Co-supervisor
Adriana Romero Soriano
Sujets de recherche
Apprentissage profond
Créativité
Raisonnement
Traitement du langage naturel
Site web
Google Scholar
GitHub

Publications

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts
Ayana Hussain
Soumya Sharma
Golnoosh Farnadi
Nicholas Vincent
Héber Hwang Arcolezi
Ulrich Aivodji
Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a maj… (voir plus)or challenge for their design and evaluation. Prior work focuses mainly on individual-level risks, overlooking \textbf{interdependent privacy (IDP)}--where one person's data may be revealed by others without their knowledge or consent. We address this gap by introducing \textbf{IDP-Bench}: the first LLM benchmark for IDP scenarios, grounded in the Contextual Integrity (CI) framework. We evaluate eight open-source LLMs on their understanding of IDP scenarios across three levels of IDP reasoning using two LLM judges. Results show strong co-ownership recognition (6/8 models exceed 90\%) but persistent weaknesses in identifying CI parameters (information attribute, primary subject) and IDP-specific parameters such as secondary subjects, where 7/8 models score below 74\%. Models also struggle to judge sharing appropriateness (5/8 scoring below 77\%). While the ability to judge the appropriateness of sharing improves with scale, performance tends to decline in smaller models, and prompt sensitivity remains high on IDP-specific questions--highlighting the need for more targeted study of IDP in LLM privacy research. Data \& code available \href{https://github.com/tisl-lab/Interdependent_Privacy_Bench}{here}.
2026-06-05
arXiv (prépublication)
doi.org
arxiv.org
Robust Reward Modeling via Causal Rubrics
Pragya Srivastava
Harman Singh
Rahul Madhavan
Gandharv Patil
Sravanti Addepalli
Arun Suggala
Rengarajan Aravamudhan
Soumya Sharma
Anirban Laha
Aravindan Raghuveer
Karthikeyan Shanmugam
Doina Precup
Reward models (RMs) for LLM alignment often exhibit reward hacking, mistaking spurious correlates (e.g., length, format) for causal quality … (voir plus)drivers (e.g., factuality, relevance), leading to brittle RMs. We introduce CROME (Causally Robust Reward Modeling), a causally-grounded framework using targeted augmentations to mitigate this. CROME employs: (1) Causal Augmentations, pairs isolating specific causal attribute changes, to enforce sensitivity, and (2) Neutral Augmentations, tie-labeled pairs varying spurious attributes while preserving causal content, to enforce invariance. Crucially, augmentations target LLM-identified causal rubrics, requiring no prior knowledge of spurious factors. CROME significantly outperforms baselines on RewardBench (Avg +5.4\%, Safety +13.2\%, Reasoning +7.2\%) and demonstrates enhanced robustness via improved Best-of-N performance across RewardBench, WildGuardTest, and GSM8k.
2025-06-09
ICML.cc/2025/Workshop/MoFA (poster)
doi.org
openreview.net